Figure REF  displays the learned graphs of the animals data set. Following the recommendations of [1]}, [2]}, the SGL algorithm is applied with \(\beta = 0.5\)  and \(\alpha =0\) , GLasso with \(\alpha =0.05\)  and the input for SGL and NGL is set to \(\frac{1}{n}\sum _{i}{x}_i^\top {x}_i + \frac{1}{3}{I}_p\) . While EGM yields results similar to GLasso, GGM provides a clearer structure with (trout, salmon) and (bee, butterfly) clustered together. Interestingly, with no assumptions regarding the number of components, GGFM and EGFM reach similar structure and modularity compared to SGL and StGL, that set a priori the number of components in the graph.
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